382 results about "Visual task" patented technology

The invention provides a multi-modal large model task processing method, device and equipment based on reinforcement learning, and the method comprises the steps: employing a multi-modal large model, generating G groups of responses for multi-modal visual task data, carrying out the scoring of each group of responses, obtaining a reward value, carrying out the standardization of the reward value, obtaining a dominance score, building a strategy updating gradient through the dominance score, and carrying out the calculation of the strategy updating gradient. A final result is obtained after expectation reward maximization, model parameter adjustment and multi-round iteration, and an expectation reward objective function comprises an expectation calculation item and KL divergence constraint, so that a current strategy can be updated by comparing the performance of candidate strategies through group relative strategy optimization, and local optimum can be helped to be jumped out; the strategy updating amplitude is limited by the KL divergence constraint, and the model is prevented from violently changing in the optimization process, so that the training stability is improved; the dynamic strategy iteration allows the model to adjust the balance between exploration and utilization according to the learning progress on the basis of keeping the stability, thereby further ensuring the effectiveness and stability of strategy optimization.

The invention discloses a bionic moving target sensing, storage and calculation integrated photoelectric detection system based on continuous photoconduction, and relates to the field of bionic intelligent vision and the field of brain-like calculation, and the system comprises a synaptic bionic photoelectric detector array and a brain-like memristor array which are interconnected. When the synaptic-like bionic photoelectric detector array detects visual information of a moving target, a fixed bias voltage is applied to a device in each pixel, space illumination information corresponding to each pixel is converted into a continuously attenuated photocurrent signal, illumination information of a multi-frame dynamic target is continuously received in the continuous attenuation process of the photocurrent, and the visual information of the moving target is detected by the synaptic-like bionic photoelectric detector array. After the last frame of illumination information is input, a light current signal of each pixel is read, and a feature map memorizing all historical moment information is determined; the brain-like memristor array is based on the Ohm's law and the Kirchhoff's law, performs deep neural network operation on the feature map, outputs a current corresponding to a task result label, identifies a dynamic target, and can meet the requirements of a deep neural network and a complex dynamic visual task.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention discloses a Token-based visual task generation method, and belongs to the technical field of intelligent task automation, and the method comprises the steps: S1, cross-modal alignment; s2, performing visual Token processing; s3, constructing a task description Token sequence: constructing the task description Token sequence based on a predefined visual task template library according to requirements of a user or a specific application scene; s4, checking task feasibility; s5, task priority scheduling; s6, training a task generation model; s7, dynamic task allocation; and S8, optimizing the model. According to the method, the long sequence processing capability is optimized through a hierarchical merging strategy, the compactness of feature expression is realized while space position information is reserved, linear projection and enhanced position coding are combined to form a visual Token sequence with strong representation capability, local detail features are contained, a global context relationship is kept, and the method is suitable for the visual Token sequence with high representation capability. High-information-density feature input is provided for subsequent task processing, and the processing precision of various visual algorithms is effectively improved.

The invention discloses a pulse neural network training method based on adaptive threshold integer activation neurons, and the method specifically comprises the following steps: inputting a neuromorphic data stream or a static image, generating a pulse sequence through time coding, and taking the pulse sequence as the input of a model; in the backbone network, performing down-sampling and extracting multi-scale features by using an SNN module based on LIF neurons activated by an adaptive threshold integer; the visual tasks are divided into image recognition, image detection and image segmentation, and are respectively accessed to different processing modules for processing and outputting; according to the invention, on the basis of a Meta-SpikeFormer element module, the spiking neural network based on LIF neurons activated by an adaptive threshold integer value is used, so that the memory overhead during training is reduced, the network performance of direct training is improved, the application range of the spiking neural network is expanded, and the network is better applied to image classification, recognition and segmentation tasks.

The invention discloses a security and protection deployment and control management method and device. The method comprises the following steps: acquiring an initial deployment and control task parameter which is input by a target object in an interactive interface and comprises a deployment and control scene, determining extension prompt information matched with the initial deployment and control task parameter by utilizing a pre-trained computer vision large model, and displaying the extension prompt information in the interactive interface, obtaining a target deployment and control task parameter which is input by the target object in the interactive interface and obtained after the initial deployment and control task parameter is expanded according to the expansion prompt information, generating a deployment and control task according to the target deployment and control task parameter, and in the process of executing the deployment and control task, obtaining a monitoring image in real time, and comprehensively analyzing the monitoring image and the target deployment and control task parameters by using a computer vision large model to obtain a deployment and control task matching result corresponding to the monitoring image. According to the method, the technical problems that a related model algorithm in a security and protection deployment and control scene can only process a specific visual task and the intelligent degree is not high are solved.

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual processing edge adaptive decision-making method for resource and task perception, and aims to solve the problem of scheduling mismatch caused by resource dynamic change and task demand diversity in visual task processing in an edge computing environment. The method comprises the following steps: collecting multi-dimensional resource state data of edge nodes in real time to form a resource state vector with high time resolution; analyzing the visual task request, and constructing a quantifiable task feature vector; and establishing a resource-task association mapping model based on a dynamic weight distribution mechanism. The method also supports cross-edge domain collaborative decision, and processes a pipeline dynamic reconstruction and security isolation mechanism. According to the technical scheme, the fluctuation of the resource utilization rate is reduced to 15% or below, the average task processing delay is reduced to 60%, the scheduling satisfaction degree is improved by 40% or above, and the self-adaptability and the service quality guarantee capability of the edge vision system are remarkably enhanced.

The invention provides a visual token pruning method based on graph information propagation. The visual token pruning method comprises the following steps: performing visual extraction on an input image to obtain a visual token; initializing the importance score of the visual token; constructing a graph structure related to the visual tokens, taking each visual token as a node, calculating an adjacent matrix for constructing connection between the visual tokens, and initializing the graph structure; the adjacency matrix is updated through a preset similarity threshold value, and visual token sub-graph structures of different areas are obtained; performing normalization processing on each row of the adjacent matrix, performing iterative propagation on node information, and calculating a final score of each visual token; screening out k visual tokens with the highest scores according to the final scores of the visual tokens for projection; and splicing the visual token obtained by projection with the text token, and inputting the spliced visual token and text token into a large language model to obtain an output result. According to the method, the calculation efficiency of the model can be improved, and the calculation cost is remarkably reduced while the visual task performance is kept.

The invention discloses an isovariant consistency image fusion method and system based on task driving, and the method comprises the steps: carrying out the feature decomposition of an input infrared image and a visible image, obtaining the basic features and detail features, and carrying out the geometric transformation operation meeting the isovariant consistency constraint in the subsequent fusion processing, so as to guarantee the transformation stability of the features. And performing semantic constraint on the fusion model by utilizing supervision information of a semantic segmentation task of the semantic segmentation model. According to the method, the equal-variant consistency constraint and the semantic loss are collaboratively optimized through the loss function part, the robustness of geometric transformation can be kept, meanwhile, the reservation of local details and the enhancement of global semantic information are effectively considered, a high-quality fused image is generated, and the performance of a downstream advanced visual task is improved.

The invention discloses a pulse neural network image classification method and system based on a space-time adaptive threshold, mainly solving the problems of poor nonlinear expression and limited time sequence and space feature processing ability in the prior art, and the scheme comprises the following steps: obtaining an image data set, and dividing the image data set into a training set and a test set; a spiking neural network main body structure comprising an input layer, a hidden layer and an output layer is selected, an existing neuron model is improved by introducing a space-time joint threshold adjustment mechanism, and improved neurons are placed in each neuron layer in the hidden layer to form a spiking neural network based on a space-time adaptive threshold. The training set is used to carry out iterative training; and inputting the test set into the trained pulse neural network to obtain an image classification result. According to the method, a space-time adaptive threshold mechanism is introduced, the threshold can be dynamically adjusted to adapt to time and space features, the processing capacity of the network on time sequence data and complex features and the classification accuracy of images are remarkably improved, and the method can be widely applied to dynamic visual tasks and event-driven scenes.

The invention belongs to the field of computer vision image signal processing, and relates to a multi-robot collaborative indoor scene semantic segmentation method and system based on multivariate interactive learning. The method comprises the steps of adopting an image feature encoder based on a double attention integration module to realize cross-modal feature fusion on an RGB image and a depth image; a dimension anisotropy attention enhancement module is adopted, and feature optimization is carried out through a multi-dimensional direction attention mechanism; a cross-task collaborative interaction module is adopted, and information is shared among different tasks by utilizing an interaction mechanism based on a feature level; in a multi-task heterogeneous decoding stage, each robot uses a decoder with a specific task to execute a specific task, and semantic segmentation, instance segmentation and scene classification are realized; and a joint optimization loss function is adopted to perform end-to-end joint training on multiple tasks, so that overall optimization and collaborative awareness performance improvement are realized. According to the method, the precision and robustness can be improved when complex indoor scene semantic segmentation and other visual tasks are executed.

The invention discloses a visual large model Token adaptive optimization method, system and device based on differential evolution and a medium, and the method comprises the steps: carrying out the data processing of an image classification data set, an instance segmentation data set and a saliency target detection data set, and obtaining all Tokens corresponding to each image through a Patch Embedding and position coding method; obtaining a plurality of groups of Tokens corresponding to each image through a random selection mode, and performing data processing to output all Tokens corresponding to each image and the plurality of groups of Tokens selected from each image; constructing a Token adaptive selection module, a self-attention optimization module and a downstream task output module; a complete Token adaptive optimization visual large model is constructed; training a reconstruction model and a complete Token self-adaptive optimized visual large model; performing model reasoning to obtain an image classification result, an instance segmentation result image and a saliency target detection result image; the system, the equipment and the medium are used for implementing the method. The method can be widely applied to various visual tasks such as image classification, instance segmentation and saliency target detection.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.

The invention discloses a multi-degraded image recovery method based on frequency domain decomposition, and aims to solve the problems that a single model is difficult to deal with various image degradation and recovery processes of different frequency domains are mutually coupled in the prior art. According to the method, a degraded image is decomposed into a high-frequency space and a low-frequency space through fast Fourier transform, and a double-branch network architecture is adopted for targeted processing: for the high-frequency part, a high-frequency feature adaptive processing module HFPM is designed, and detail texture features are effectively extracted and interference is suppressed through feature enhancement and cross-layer fusion technologies; and for the low-frequency part, constructing a low-frequency feature conversion enhancement module LTEM, and capturing global context information by using cyclic convolution to improve the integrity of the structure contour. According to the method, decoupling processing of frequency domain features is realized, and the image restoration performance of the model in various degradation scenes such as rain removal, noise removal and defogging is remarkably improved through the synergistic effect of high-frequency detail enhancement and low-frequency structure optimization. Experimental results show that the method has excellent recovery effect and robustness when a plurality of image degradation tasks are processed at the same time, and can be effectively applied to visual tasks such as traffic accidents with high image quality requirements.

The invention provides a visual task processing method and device based on a multi-modal large model of low-rank adaptive enhancement, and relates to the technical field of data processing. The method comprises the following steps: receiving a plurality of visual task requests sent by a visual application and visual input data corresponding to each visual task request in the plurality of visual task requests; dynamically scheduling the plurality of visual task requests into a plurality of batches of processing sequences through an orchestrator, and determining a reasoning mode of a low-rank adapter used by each batch of visual input data; and inputting the visual input data of each batch and the corresponding low-rank adapter into a target multi-modal large model, and performing dynamic fragmentation calculation on the visual input data and a decomposition matrix of the corresponding low-rank adapter through an adaptive fragmentation matrix multiplication operator ATMM to obtain a response result corresponding to each visual task request. Through the method provided by the invention, diversified visual task requirements are met.

The invention discloses an infrared and visible light image fusion method based on target perception, and the method comprises the following steps: S1, obtaining a training data set which comprises a plurality of input image pairs composed of infrared images and visible light images; s2, constructing an image fusion depth model; s3, training the image fusion depth model based on the training data set to obtain a target image fusion depth model; and S4, fusing the to-be-fused image pair by using the target image fusion depth model to obtain a fused image result. According to the invention, through the target sensing region fusion sub-model, the space-frequency attention sub-model and the fusion block sub-model, differential feature extraction and fusion are realized, and the definition, detail retention and downstream visual task performance of the fused image are improved.

The present invention relates to the field of computer vision technology, and specifically to an infrared and visible light image fusion method with adaptive feature extraction, comprising: using a single-modal spatial adaptive feature mixing module to perform multi-scale spatial adaptive feature extraction on two modal images; inputting the extracted features into a dual-branch feature extraction module to obtain low-frequency global features and high-frequency local features; then splicing the dual-modal features, and reconstructing the fused image using a dual-modal spatial adaptive feature fusion module. In order to solve the imbalance problem between modality-specific features and shared features, decomposition loss is introduced into the loss function to further optimize the fusion model. The present invention adopts a dual-branch feature extraction module to establish local and global contextual dependencies, and extracts potential features of the modalities at multiple scales to generate a fused image with rich details and global consistency, which can perform well even in harsh environments and improve the effects of downstream visual tasks.

The invention discloses a positive feedback self-supervised learning method based on characterization of self-distribution and clustering attributes, and belongs to the field of computer vision, and the method specifically comprises the steps: firstly, building a joint embedded architecture JEA composed of a data enhancement module, an encoder, a projection head and an optional prediction head; then, an input set D of given images randomly samples a group of images and inputs the images into a data enhancement module to obtain enhancement results, then the enhancement results respectively pass through an encoder to obtain corresponding encoding results, and then the enhancement results respectively pass through a projection head to obtain corresponding projection results; for the online network, based on an output result of the projection head, calculating an output result after passing through the prediction head; calculating and optimizing a cross entropy loss function on the basis of an output result of each module of the joint embedded architecture JEA so as to update parameters of the JEA, and finally performing performance evaluation on the trained model on a computer vision task; according to the method, inherent clustering attributes of model coding are utilized, and the model performance is further improved through positive feedback learning.

The invention discloses a YOLO model parameter efficient fine tuning method based on low-rank self-adaption. Aiming at the problems that a traditional full-parameter fine tuning method is high in calculation cost and large in storage pressure and an existing efficient parameter fine tuning method is insufficient in adaptation in a visual task, a low-rank adaptive layer is introduced into a convolutional layer of a YOLO model, a weight update quantity is decomposed into a low-rank matrix product, and a feature fusion process is optimized in combination with a lightweight adapter. In specific implementation, customized improvement is performed on a core module of the YOLO model, including multi-scale feature fusion optimization, attention weight dynamic adjustment and the like, and meanwhile, pre-training parameters are frozen and only a low-rank matrix is updated during training. The parameter efficiency and the detection performance are effectively balanced under the condition that only a small number of parameters are finely adjusted, and the method is suitable for edge device deployment and real-time detection scenes.

The invention is suitable for the technical field of computer vision, and provides an unsupervised low-illumination image multi-degradation problem recovery method based on CNN-Transform, which constructs a network comprising a low-illumination image enhancement and exposure suppression module and an image denoising module. Adjusting the brightness pixel by pixel and suppressing overexposure by using a low light enhancement and exposure suppression curve; and the latter realizes noise removal through noise addition processing and a C-T module. The design comprises seven unsupervised loss functions, and training can be carried out without pairwise labeling data. The advantages of CNN local feature extraction and Transform global dependence modeling are combined, and the lightweight and real-time performance of the model are ensured. According to the method, the image brightness is effectively enhanced, overexposure is inhibited, noise is removed, a high-quality image basis is provided for the fields of automatic driving, security and protection monitoring, medical images and the like, and meanwhile advanced visual tasks such as target detection and the like are assisted.

The invention relates to a pooling and up-sampling fusion CAM method based on information entropy and an attention mechanism. The method comprises the steps of image preprocessing, feature extraction, information entropy calculation, attention weight matrix design, weighted feature graph and pooling, up-sampling and residual connection, category weight assignment, CAM generation and the like. By introducing the information entropy theory, the complexity and importance of information in the feature map can be accurately evaluated, attention weight design is guided, key information is focused, redundancy is suppressed, and the capability of processing complex images by the model is remarkably improved. And in combination with an attention mechanism, feature representation is more effective and accurate. Through pooling and up-sampling technology fusion, the size of a feature map is flexibly adjusted, key features are reserved, and subsequent tasks are facilitated. Through the application of global average pooling and a pre-training classifier, a class activation mapping graph is generated, the performance and accuracy of a computer vision task are improved, and the explanatory performance of a model on picture processing is improved.

The invention discloses a dynamic self-adaptive edge server visual task processing method and system, and belongs to the technical field of visual task processing, and the method comprises the steps: constructing an edge load model and an energy risk model, outputting an edge load rate and an energy risk coefficient, and generating a local execution confidence coefficient based on the edge load rate and the energy risk coefficient; generating a cloud execution confidence coefficient in combination with the network index; quantizing the data quality and the environment state through the data evaluation model and the data acquisition environment model; fusing the data timeliness factors to construct a local-data adaptation degree and a cloud-data adaptation degree; local / cloud execution is decided according to the decision model, and the resolution is optimized based on the execution adaptation degree. According to the method, resource dynamic adaptation and task quality optimization are realized, and the edge visual processing efficiency is remarkably improved.

The invention discloses a machine vision-oriented three-dimensional image coding method, which comprises the following steps of: performing feature extraction on input left and right views by using a three-dimensional image feature extraction module to obtain three-dimensional image multi-scale features containing 3D scene information; a stereo image multi-scale feature coding module is used for coding the stereo image multi-scale features to obtain compact left and right viewpoint potential representations, and stereo entropy coding is carried out on the left and right viewpoint potential representations to obtain code streams for transmission; performing stereo entropy decoding on the transmitted code stream to obtain decoded left and right viewpoint potential representations, and decoding the left and right viewpoint potential representations at a decoding end by using a stereo image multi-scale feature decoding module to obtain reconstructed stereo image multi-scale features; and performing visual analysis on the reconstructed stereo image multi-scale features by using a 3D visual analysis module to obtain a 3D target detection result. According to the invention, the 3D visual task performance and the stereo image compression efficiency are effectively improved.

The invention provides an image enhancement method, system and device for an automatic driving vision task, and belongs to the technical field of combination of computer vision and automatic driving, and the method comprises the steps: obtaining a multi-view image of a to-be-detected target, and splicing adjacent view images; applying a first size grid mask on the spliced image; removing a mask in an overlapping region of the spliced image to obtain an intermediate image; detecting a preset target area in the intermediate image, and applying a second size grid mask to the preset target area; and replacing the first size grid mask at the corresponding position with the second size grid mask to obtain an enhanced image. Based on the method, the invention further provides an image enhancement system for the automatic driving vision task. According to the method, a multi-view grid mask enhancement method is adopted, the first-size mask and the second-size mask are used for carrying out global subject covering and local small target covering respectively, and the robustness and generalization ability of a visual perception model in an automatic driving system in a complex environment are improved.

The invention relates to an adversarial patch defense method based on a visual task, also relates to an adversarial patch defense device and system based on the visual task, and belongs to the technical field of computer vision and deep learning. The method comprises the following steps: constructing an adversarial patch detection data set and an adversarial patch completion data set; utilizing data set training to respectively obtain an adversarial patch detection model and a complementation model; positioning and replacing an adversarial patch area in the picture through the detection model; and complementing the incomplete image after the patch area is removed through a complementing model. According to the method, a novel detection and completion-based adversarial patch defense model is provided, and the model can be combined with various visual models and is applied to a multi-visual task. Foreground and background judgment is introduced into an adversarial patch detection model, system resources can be saved, and model efficiency can be improved. According to the method, the patch attack can be effectively defended under the condition that the structure of the existing visual task model is not changed.

The invention discloses a field generalization semantic segmentation enhancement method based on an efficient multi-scale and simple attention module, and belongs to the field of computer vision. According to the invention, the model is based on a TQDM (Text Query-Driven Mask Transfer) framework, and an EMA (Efficient Multi-Scale Attention Module) and a SimAM (Simple Parameter-free Attention Module) are fused, so that the adaptability and the robustness of a semantic segmentation task on a plurality of domains are improved. According to the invention, by introducing the EMA module, multi-scale feature aggregation and cross-space information interaction are realized, so that the understanding ability of the model to a complex scene is enhanced; meanwhile, in combination with a SimAM module, feature expression is optimized under the condition of not increasing extra parameters, and the precision of small target segmentation and target boundary detection is improved. The method can be widely applied to computer vision tasks related to cross-domain semantic segmentation, such as automatic driving, intelligent monitoring and medical image analysis.

The invention relates to a double-graph neural network framework, namely a Vision DualGNN (VDG), for a visual task. Aiming at the problems of scattered object neighbor distribution and insufficient graph processing caused by the fact that an existing Vision GNN (ViG) method only depends on semantic information when a graph structure is constructed, the VDG is used for respectively processing graphs constructed based on space and semantics by fusing space and semantic information and adopting a double-flow graph neural network architecture, so that the graph structure is constructed based on the space and the semantics. Therefore, the expression ability of the node features in the image and the expression effect of the graph level are effectively improved. Specifically, the method comprises the following steps: 1) converting an image block into a node feature with high expressivity by using a node encoder; (2) a double-graph structure is constructed, a spatial graph serves as a constraint condition of a semantic graph, and the spatial perception ability of node features is enhanced; experimental results show that the VDG model provided by the invention has better performance than other baseline models on ImageNet and CIFAR-100 data sets, and the effectiveness and superiority of the VDG model in a visual task are proved.

The invention provides a target segmentation recognition method based on visible light and infrared modal fusion, which comprises the following steps of: 1, acquiring an infrared image and recording the infrared image as ii, and recording a visible light image as iv; step 2, performing registration preprocessing on the infrared image as ii and the visible light image as iv to obtain an infrared registration image as iir and a visible light registration image as ivis; step 3, pre-training the fusion network; 4, recording the infrared registration image as iir and the visible light registration image as ivis, inputting the images into a fusion network for fusion, and outputting a fusion image; 5, carrying out semantic segmentation on the fused image, and carrying out target identification and positioning; according to the method, the calculation pressure of a multi-source perception fusion module is reduced, and the dual-light image fusion efficiency based on the semantic fusion network is improved. Moreover, loss function limitation of manual subjective design can be avoided, the high-quality visual effect is guaranteed, the performance of advanced visual tasks is remarkably improved, and meanwhile the application value and effect of the image fusion technology are improved.

A system for performing a three-dimensional computer vision task using a neural radiance field grid representation of a scene produced from two-dimensional images of at least a portion of the scene can include a processor and a memory. A neural radiance field grid network module can produce, from the two-dimensional images, three-dimensional patches of the neural radiance field grid representation of the scene. A three-dimensional shifted window visual transformer module can produce, from the three-dimensional patches, a feature map. A first decoder module can produce, from the feature map, the neural radiance field grid representation of the scene to train the system. A second decoder module can produce, from the feature map, the neural radiance field grid representation of the scene to perform the three-dimensional computer vision task for a cyber-physical system.